Create app.py

app.py

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+import streamlit as st
+import os
+import logging
+from io import BytesIO
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.prompts import PromptTemplate
+from langchain.chains.question_answering import load_qa_chain
+from langchain_community.llms import HuggingFaceHub
+from transformers import pipeline  # For fallback if Hub fails
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Check API token
+if "HUGGINGFACEHUB_API_TOKEN" not in os.environ:
+    st.error("HUGGINGFACEHUB_API_TOKEN not set in secrets. Add it in Space settings.")
+    st.stop()
+
+try:
+    # Function to process PDF
+    def process_pdf(uploaded_file):
+        try:
+            logger.info("Starting PDF processing")
+            pdf_reader = PdfReader(BytesIO(uploaded_file.getvalue()))
+            text = ""
+            for page in pdf_reader.pages:
+                extracted = page.extract_text()
+                if extracted:
+                    text += extracted + "\n"
+            
+            if not text:
+                raise ValueError("No text extracted from PDF.")
+            
+            # Chunk text (increased overlap for better context)
+            text_splitter = CharacterTextSplitter(separator="\n", chunk_size=800, chunk_overlap=200, length_function=len)
+            chunks = text_splitter.split_text(text)
+            
+            # Embeddings (light model)
+            embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={'device': 'cpu'})
+            
+            # Vector store
+            vector_store = FAISS.from_texts(chunks, embedding=embeddings)
+            logger.info("PDF processed successfully")
+            return vector_store
+        except Exception as e:
+            logger.error(f"PDF processing error: {str(e)}")
+            st.error(f"Error processing PDF: {str(e)}")
+            return None
+
+    # Function to answer questions
+    def answer_question(vector_store, query):
+        try:
+            logger.info(f"Answering query: {query}")
+            # Lighter LLM via pipeline for faster CPU inference
+            qa_pipeline = pipeline("text2text-generation", model="google/flan-t5-base")
+            
+            # Retrieve top chunks
+            docs = vector_store.similarity_search(query, k=3)
+            context = "\n".join([doc.page_content for doc in docs])
+            
+            # Prompt
+            prompt = f"Use this context to answer concisely: {context}\nQuestion: {query}\nAnswer:"
+            response = qa_pipeline(prompt, max_length=256, num_return_sequences=1)[0]['generated_text']
+            
+            logger.info("Answer generated")
+            return response.strip()
+        except Exception as e:
+            logger.error(f"Answer generation error: {str(e)}")
+            st.error(f"Error answering: {str(e)}")
+            return "Unable to generate answer."
+
+    # Streamlit UI with chat history
+    st.title("Smart PDF Q&A")
+    st.write("Upload a PDF and ask questions! Chat history is preserved.")
+
+    # Initialize session state
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+    if "vector_store" not in st.session_state:
+        st.session_state.vector_store = None
+
+    # PDF upload and process
+    uploaded_file = st.file_uploader("Upload PDF", type="pdf")
+    if uploaded_file:
+        if st.button("Process PDF"):
+            with st.spinner("Processing..."):
+                vector_store = process_pdf(uploaded_file)
+                if vector_store:
+                    st.session_state.vector_store = vector_store
+                    st.success("PDF ready! Ask away.")
+                    st.session_state.messages = []  # Reset chat on new PDF
+
+    # Display chat history
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    # Question input
+    if st.session_state.vector_store:
+        if prompt := st.chat_input("Ask a question:"):
+            # Add user message
+            st.session_state.messages.append({"role": "user", "content": prompt})
+            with st.chat_message("user"):
+                st.markdown(prompt)
+            
+            # Generate answer
+            with st.chat_message("assistant"):
+                with st.spinner("Thinking..."):
+                    answer = answer_question(st.session_state.vector_store, prompt)
+                st.markdown(answer)
+            st.session_state.messages.append({"role": "assistant", "content": answer})
+
+except Exception as e:
+    logger.error(f"App initialization failed: {str(e)}")
+    st.error(f"Initialization error: {str(e)}. Check logs or try factory reset.")